##Description The LSTM model is trained on the flickr8k dataset using precomputed VGG features from http://cs.stanford.edu/people/karpathy/deepimagesent/. Model details can be found in the following CVPR-2015 paper:
Show and tell: A neural image caption generator. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. CVPR, 2015 (arXiv ref. cs1411.4555)
The model was trained for 15 epochs where 1 epoch is 1 pass over all 5 captions of each image. Training data was shuffled each epoch. To evaluate on the test set, download the model and weights, and run:
python image_caption.py --model_file [path_to_weights]
##Performance For testing, the model is only given the image and must predict the next word until a stop token is predicted. Greedy search is currently used by just taking the max probable word each time. Using the bleu score evaluation script from https://raw.githubusercontent.com/karpathy/neuraltalk/master/eval/ and evaluating against 5 reference sentences the results are below.
A few things that were not implemented are beam search, l2 regularization, and ensembles. With these things, performance would be a bit better.